The natural history of trachoma infection and disease in a Gambian cohort with frequent follow-up

Abstract

Background: The natural history of ocular Chlamydia trachomatis infections in endemic communities has not been well characterised and is an important determinant of the effectiveness of different mass treatment strategies to prevent blindness due to trachoma.

Methodology/principal findings: A multistate hidden Markov model was fitted to data on infection and active disease from 256 untreated villagers in The Gambia who were examined every 2 weeks over a 6-month period. Parameters defining the natural history of trachoma were estimated, and associations between these parameters, demographic and baseline immune measurements examined. The median incubation period following infection was estimated at 17 days (95% confidence interval: 11-28). Disease persisted for longer than infection (median 21 (15-32) weeks) versus 17 (12-24) weeks), with an estimated median duration of post-infection inflammation of 5 (3-8) weeks. The duration of active disease showed a significant decline with age even after accounting for lower rates of re-infection and disease at older ages (p = 0.004). Measurements of levels of baseline IgA to epitopes in the major outer membrane protein of Chlamydia trachomatis were not significantly correlated with protection or more rapid clearance of infection.

Conclusions: The average duration of infection with Chlamydia trachomatis especially at younger ages is long. This contributes to the persistence and gradual return of trachoma after community-wide treatment with antibiotics.

Figure 1. Markov models of the natural history of active trachoma.

A) A simple model of infection with a constant hazard of becoming infected λ or recovering from infection ν. This same two-state model can be used to describe the process of the onset and resolution of inflammatory disease. B) A four state model of infection and disease with states indexed from 1 to 4. In this case transition intensities for the infection and disease processes are mutually dependent. For example, the probability of clearing infection depends on whether inflammatory disease is present. The ‘standard’ model of trachoma natural history corresponds to the solid arrows with transition parameters marked. This is illustrated in C), where time runs from left to right and disease states are indicated at the top of the figure.

Figure 2. Likelihood of the hidden Markov model for active disease or infection, for different values of the median duration and test sensitivity plotted about the maximum likelihood estimate.

The model for active disease is shown in A) and infection in B). In each plot the duration of disease or infection is plotted on a log-scale and sensitivity on the logit scale.

Figure 3. Development of active disease (TF/TI) after ocular infection with C. trachomatis estimated from cohort data under the standard model of trachoma natural history.

The fraction of individuals who remain infected (solid line) or who have active disease (dashed line) is plotted against the time since infection. The shaded areas highlight individuals with incubating infections or post-infection inflammation (active disease). The parameter estimates are from Table 2.